The Real-Life Application of Differential Evolution with a Distance-Based Mutation-Selection

This paper proposes the real-world application of the Differential Evolution (DE) algorithm using, distance-based mutation-selection, population size adaptation, and an archive for solutions (DEDMNA). This simple framework uses three widely-used mutation types with the application of binomial crosso...

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Main Author: Petr Bujok
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Mathematics
Subjects:
Online Access:https://www.mdpi.com/2227-7390/9/16/1909
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spelling doaj-49c49288fed440958fd8afec1b3079c72021-08-26T14:02:11ZengMDPI AGMathematics2227-73902021-08-0191909190910.3390/math9161909The Real-Life Application of Differential Evolution with a Distance-Based Mutation-SelectionPetr Bujok0Department of Informatics and Computers, Faculty of Science, University of Ostrava, 30. Dubna 22, 70103 Ostrava, Czech RepublicThis paper proposes the real-world application of the Differential Evolution (DE) algorithm using, distance-based mutation-selection, population size adaptation, and an archive for solutions (DEDMNA). This simple framework uses three widely-used mutation types with the application of binomial crossover. For each solution, the most proper position prior to evaluation is selected using the Euclidean distances of three newly generated positions. Moreover, an efficient linear population-size reduction mechanism is employed. Furthermore, an archive of older efficient solutions is used. The DEDMNA algorithm is applied to three real-life engineering problems and 13 constrained problems. Seven well-known state-of-the-art DE algorithms are used to compare the efficiency of DEDMNA. The performance of DEDMNA and other algorithms are comparatively assessed using statistical methods. The results obtained show that DEDMNA is a very comparable optimiser compared to the best performing DE variants. The simple idea of measuring the distance of the mutant solutions increases the performance of DE significantly.https://www.mdpi.com/2227-7390/9/16/1909differential evolutiondistance-basedmutation-selectionreal applicationexperimental studyglobal optimisation
collection DOAJ
language English
format Article
sources DOAJ
author Petr Bujok
spellingShingle Petr Bujok
The Real-Life Application of Differential Evolution with a Distance-Based Mutation-Selection
Mathematics
differential evolution
distance-based
mutation-selection
real application
experimental study
global optimisation
author_facet Petr Bujok
author_sort Petr Bujok
title The Real-Life Application of Differential Evolution with a Distance-Based Mutation-Selection
title_short The Real-Life Application of Differential Evolution with a Distance-Based Mutation-Selection
title_full The Real-Life Application of Differential Evolution with a Distance-Based Mutation-Selection
title_fullStr The Real-Life Application of Differential Evolution with a Distance-Based Mutation-Selection
title_full_unstemmed The Real-Life Application of Differential Evolution with a Distance-Based Mutation-Selection
title_sort real-life application of differential evolution with a distance-based mutation-selection
publisher MDPI AG
series Mathematics
issn 2227-7390
publishDate 2021-08-01
description This paper proposes the real-world application of the Differential Evolution (DE) algorithm using, distance-based mutation-selection, population size adaptation, and an archive for solutions (DEDMNA). This simple framework uses three widely-used mutation types with the application of binomial crossover. For each solution, the most proper position prior to evaluation is selected using the Euclidean distances of three newly generated positions. Moreover, an efficient linear population-size reduction mechanism is employed. Furthermore, an archive of older efficient solutions is used. The DEDMNA algorithm is applied to three real-life engineering problems and 13 constrained problems. Seven well-known state-of-the-art DE algorithms are used to compare the efficiency of DEDMNA. The performance of DEDMNA and other algorithms are comparatively assessed using statistical methods. The results obtained show that DEDMNA is a very comparable optimiser compared to the best performing DE variants. The simple idea of measuring the distance of the mutant solutions increases the performance of DE significantly.
topic differential evolution
distance-based
mutation-selection
real application
experimental study
global optimisation
url https://www.mdpi.com/2227-7390/9/16/1909
work_keys_str_mv AT petrbujok thereallifeapplicationofdifferentialevolutionwithadistancebasedmutationselection
AT petrbujok reallifeapplicationofdifferentialevolutionwithadistancebasedmutationselection
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